"Automatic Feature Extraction in Images and Texts using Transfer Learning"

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Project links

Homepage

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Development Status
- 3 - Alpha
Intended Audience
- Developers
License
- OSI Approved :: Apache Software License
Natural Language
- English
Programming Language

Project description

`deepfeatx`: Deep Learning Feature Extractor of Images using Transfer Learning Models

Helper for automatic extraction of features from images (and soon text as well) from transfer learning models like ResNet, VGG16 and EfficientNet.

Install

#hide_output
!pip install deepfeatx

Why this project has been created

Fill the gap between ML and DL thus allowing estimators beyond only neural networks for computer vision and NLP problems
Neural network models are too painful to setup and train - data generators, optimizers, learning rates, loss functions, training loops, batch size, etc.
State of the art results are possible thanks to pretrained models that allows feature extraction
With this library we can handle those problems as they were traditional machine learning problems
Possibility of using low-code APIs like scikit-learn for computer vision and NLP problems

Usage

Extracting features from an image

from deepfeatx.image import ImageFeatureExtractor
fe = ImageFeatureExtractor()

2021-10-06 11:27:12.595100: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2021-10-06 11:27:12.595191: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)


Metal device set to: Apple M1

im_url='https://raw.githubusercontent.com/WittmannF/deepfeatx/master/sample_data/cats_vs_dogs/valid/dog/dog.124.jpg'
fe.read_img_url(im_url)

png

fe.url_to_vector(im_url)

2021-10-06 11:27:13.679687: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
2021-10-06 11:27:13.679874: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
2021-10-06 11:27:13.846942: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.





array([[0.282272  , 1.0504342 , 0.11333481, ..., 0.18499802, 0.02220213,
        0.06158632]], dtype=float32)

Extracting Features from a Folder with Images

!git clone https://github.com/WittmannF/image-scraper.git

fatal: destination path 'image-scraper' already exists and is not an empty directory.

df=fe.extract_features_from_directory('image-scraper/images/pug',
                                   classes_as_folders=False,
                                   export_vectors_as_df=True)

df.head()

Found 4 validated image filenames.
1/1 [==============================] - 0s 412ms/step


2021-10-06 11:27:16.893822: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.

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	filepaths	0	1	2	3	4	5	6	7	8	...	2038	2039	2040	2041	2042	2043	2044	2045	2046	2047
0	image-scraper/images/pug/efd08a2dc5.jpg	0.030705	0.042393	0.422986	1.316509	0.020907	0.000000	0.081956	0.404423	0.489835	...	0.013765	0.642072	1.818821	0.299441	0.000000	0.419997	0.200106	0.179524	0.026852	0.079208
1	image-scraper/images/pug/24d0f1eee3.jpg	0.068498	0.319734	0.081250	1.248270	0.035602	0.003398	0.000000	0.131528	0.099514	...	0.258502	1.042543	0.691716	0.264937	0.112621	0.927995	0.050389	0.000000	0.087217	0.066992
2	image-scraper/images/pug/6fb189ce56.jpg	0.373005	0.102008	0.097662	0.362927	0.549803	0.118015	0.000000	0.104320	0.102526	...	0.210635	0.213147	0.013510	0.574433	0.017234	0.628009	0.000000	0.184550	0.000000	0.248099
3	image-scraper/images/pug/ee815ebc87.jpg	0.263904	0.430294	0.391808	0.033076	0.200174	0.019310	0.002792	0.129120	0.050257	...	0.048244	0.147806	1.430154	0.266686	0.005126	0.158225	0.097526	0.005045	0.060016	1.109626

4 rows × 2049 columns

Extracting Features from a directory having one sub-folder per class

If the directory structure is the following:

main_directory/
...class_a/
......a_image_1.jpg
......a_image_2.jpg
...class_b/
......b_image_1.jpg
......b_image_2.jpg

We can enter main_directory as input by changing classes_as_folders as True:

df=fe.extract_features_from_directory('image-scraper/images/',
                                      classes_as_folders=True,
                                      export_vectors_as_df=True,
                                      export_class_names=True)

df.head()

Found 504 images belonging to 6 classes.


2021-10-06 11:27:22.669056: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.


16/16 [==============================] - 6s 358ms/step

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	filepaths	classes	0	1	2	3	4	5	6	7	...	2038	2039	2040	2041	2042	2043	2044	2045	2046	2047
0	image-scraper/images/chihuahua/00dcf98689.jpg	chihuahua	0.640897	0.887126	0.017012	0.723459	0.164907	0.010150	0.042344	0.987457	...	0.289271	0.182086	0.638064	0.092432	0.212789	0.077480	0.255031	0.006371	0.489620	0.028672
1	image-scraper/images/chihuahua/01ee02c2fb.jpg	chihuahua	0.357992	0.128552	0.227736	0.652588	0.014283	0.092680	0.049545	0.319637	...	0.061090	0.526585	2.363337	0.160859	0.000000	0.008739	0.401081	1.377398	0.383465	0.434211
2	image-scraper/images/chihuahua/040df01fb4.jpg	chihuahua	0.163308	0.383921	0.029490	0.985443	0.866045	0.098337	0.000000	0.634062	...	0.188044	0.000000	0.056569	1.115319	0.000000	0.005084	0.072280	0.555855	0.333000	0.413303
3	image-scraper/images/chihuahua/04d8487a97.jpg	chihuahua	0.206927	3.128521	0.147507	0.104669	0.554029	2.415109	0.009964	0.171642	...	0.000000	1.297839	1.165449	0.562891	0.000000	0.395750	0.250796	0.295067	0.534072	0.051334
4	image-scraper/images/chihuahua/0d9fa44dea.jpg	chihuahua	0.233232	0.355028	0.453336	0.060354	0.479405	0.000000	0.099390	0.223719	...	0.308505	0.376597	1.075250	0.416980	0.073678	0.316829	0.620357	0.125714	0.179848	0.110405

5 rows × 2050 columns

The usage of export_class_names=True will add a new column to the dataframe with the classes names.

Examples

Cats vs Dogs using Keras vs `deepfeatx`

First let's compare the code of one of the simplest deep learning libraries (Keras) with deepfeatx. As example, let's use a subset of Cats vs Dogs:

from deepfeatx.image import download_dataset
download_dataset('https://github.com/dl7days/datasets/raw/master/cats-dogs-data.zip', 'cats-dogs-data.zip')

Downloading Dataset...


--2021-10-06 11:26:20--  https://github.com/dl7days/datasets/raw/master/cats-dogs-data.zip
Resolving github.com (github.com)... 20.201.28.151
Connecting to github.com (github.com)|20.201.28.151|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://raw.githubusercontent.com/dl7days/datasets/master/cats-dogs-data.zip [following]
--2021-10-06 11:26:20--  https://raw.githubusercontent.com/dl7days/datasets/master/cats-dogs-data.zip
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.108.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 55203029 (53M) [application/zip]
Saving to: ‘cats-dogs-data.zip’

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  8700K .......... .......... .......... .......... .......... 16% 14,3M 2s
  8750K .......... .......... .......... .......... .......... 16%  169M 2s
  8800K .......... .......... .......... .......... .......... 16% 20,2M 2s
  8850K .......... .......... .......... .......... .......... 16% 19,2M 2s
  8900K .......... .......... .......... .......... .......... 16% 57,5M 2s
  8950K .......... .......... .......... .......... .......... 16% 54,4M 2s
  9000K .......... .......... .......... .......... .......... 16% 26,1M 2s
  9050K .......... .......... .......... .......... .......... 16% 22,6M 2s
  9100K .......... .......... .......... .......... .......... 16% 78,6M 2s
  9150K .......... .......... .......... .......... .......... 17% 14,3M 2s
  9200K .......... .......... .......... .......... .......... 17% 79,4M 2s
  9250K .......... .......... .......... .......... .......... 17% 16,2M 2s
  9300K .......... .......... .......... .......... .......... 17% 68,1M 2s
  9350K .......... .......... .......... .......... .......... 17% 27,8M 2s
  9400K .......... .......... .......... .......... .......... 17% 43,3M 2s
  9450K .......... .......... .......... .......... .......... 17% 29,3M 2s
  9500K .......... .......... .......... .......... .......... 17% 45,5M 2s
  9550K .......... .......... .......... .......... .......... 17% 21,6M 2s
  9600K .......... .......... .......... .......... .......... 17% 13,5M 2s
  9650K .......... .......... .......... .......... .......... 17%  152M 2s
  9700K .......... .......... .......... .......... .......... 18% 33,2M 2s
  9750K .......... .......... .......... .......... .......... 18% 94,6M 2s
  9800K .......... .......... .......... .......... .......... 18% 19,4M 2s
  9850K .......... .......... .......... .......... .......... 18% 67,9M 2s
  9900K .......... .......... .......... .......... .......... 18% 15,5M 2s
  9950K .......... .......... .......... .......... .......... 18% 52,4M 2s
 10000K .......... .......... .......... .......... .......... 18% 12,6M 2s
 10050K .......... .......... .......... .......... .......... 18%  168M 2s
 10100K .......... .......... .......... .......... .......... 18% 21,4M 2s
 10150K .......... .......... .......... .......... .......... 18%  137M 2s
 10200K .......... .......... .......... .......... .......... 19% 20,5M 2s
 10250K .......... .......... .......... .......... .......... 19% 5,43M 2s
 10300K .......... .......... .......... .......... .......... 19%  105M 2s
 10350K .......... .......... .......... .......... .......... 19%  126M 2s
 10400K .......... .......... .......... .......... .......... 19%  157M 2s
 10450K .......... .......... .......... .......... .......... 19%  157M 2s
 10500K .......... .......... .......... .......... .......... 19% 85,2M 2s
 10550K .......... .......... .......... .......... .......... 19% 87,0M 2s
 10600K .......... .......... .......... .......... .......... 19%  168M 2s
 10650K .......... .......... .......... .......... .......... 19%  156M 2s
 10700K .......... .......... .......... .......... .......... 19% 24,3M 2s
 10750K .......... .......... .......... .......... .......... 20% 80,7M 2s
 10800K .......... .......... .......... .......... .......... 20% 13,7M 2s
 10850K .......... .......... .......... .......... .......... 20% 17,5M 2s
 10900K .......... .......... .......... .......... .......... 20%  183M 2s
 10950K .......... .......... .......... .......... .......... 20% 19,8M 2s
 11000K .......... .......... .......... .......... .......... 20% 39,6M 2s
 11050K .......... .......... .......... .......... .......... 20% 37,5M 2s
 11100K .......... .......... .......... .......... .......... 20% 19,6M 2s
 11150K .......... .......... .......... .......... .......... 20%  152M 2s
 11200K .......... .......... .......... .......... .......... 20% 22,8M 2s
 11250K .......... .......... .......... .......... .......... 20% 44,3M 2s
 11300K .......... .......... .......... .......... .......... 21% 16,1M 2s
 11350K .......... .......... .......... .......... .......... 21% 55,4M 2s
 11400K .......... .......... .......... .......... .......... 21% 20,7M 2s
 11450K .......... .......... .......... .......... .......... 21%  103M 2s
 11500K .......... .......... .......... .......... .......... 21% 23,3M 2s
 11550K .......... .......... .......... .......... .......... 21% 95,4M 2s
 11600K .......... .......... .......... .......... .......... 21% 11,1M 2s
 11650K .......... .......... .......... .......... .......... 21%  175M 2s
 11700K .......... .......... .......... .......... .......... 21% 28,4M 2s
 11750K .......... .......... .......... .......... .......... 21%  103M 2s
 11800K .......... .......... .......... .......... .......... 21% 23,8M 2s
 11850K .......... .......... .......... .......... .......... 22% 68,7M 2s
 11900K .......... .......... .......... .......... .......... 22% 20,9M 2s
 11950K .......... .......... .......... .......... .......... 22% 51,0M 2s
 12000K .......... .......... .......... .......... .......... 22% 5,65M 2s
 12050K .......... .......... .......... .......... .......... 22%  117M 2s
 12100K .......... .......... .......... .......... .......... 22%  141M 2s
 12150K .......... .......... .......... .......... .......... 22%  139M 2s
 12200K .......... .......... .......... .......... .......... 22%  164M 2s
 12250K .......... .......... .......... .......... .......... 22% 54,0M 2s
 12300K .......... .......... .......... .......... .......... 22% 75,5M 2s
 12350K .......... .......... .......... .......... .......... 23% 22,6M 2s
 12400K .......... .......... .......... .......... .......... 23% 8,03M 2s
 12450K .......... .......... .......... .......... .......... 23% 80,7M 2s
 12500K .......... .......... .......... .......... .......... 23%  147M 2s
 12550K .......... .......... .......... .......... .......... 23%  121M 2s
 12600K .......... .......... .......... .......... .......... 23%  143M 2s
 12650K .......... .......... .......... .......... .......... 23% 33,3M 2s
 12700K .......... .......... .......... .......... .......... 23% 17,4M 2s
 12750K .......... .......... .......... .......... .......... 23% 62,4M 2s
 12800K .......... .......... .......... .......... .......... 23% 20,1M 2s
 12850K .......... .......... .......... .......... .......... 23% 82,5M 2s
 12900K .......... .......... .......... .......... .......... 24% 23,9M 2s
 12950K .......... .......... .......... .......... .......... 24% 40,5M 2s
 13000K .......... .......... .......... .......... .......... 24% 16,8M 2s
 13050K .......... .......... .......... .......... .......... 24%  139M 2s
 13100K .......... .......... .......... .......... .......... 24% 22,1M 2s
 13150K .......... .......... .......... .......... .......... 24% 87,8M 2s
 13200K .......... .......... .......... .......... .......... 24% 13,0M 2s
 13250K .......... .......... .......... .......... .......... 24% 95,2M 2s
 13300K .......... .......... .......... .......... .......... 24% 20,0M 2s
 13350K .......... .......... .......... .......... .......... 24%  124M 2s
 13400K .......... .......... .......... .......... .......... 24% 18,1M 2s
 13450K .......... .......... .......... .......... .......... 25%  148M 2s
 13500K .......... .......... .......... .......... .......... 25% 19,0M 2s
 13550K .......... .......... .......... .......... .......... 25% 15,6M 2s
 13600K .......... .......... .......... .......... .......... 25%  131M 2s
 13650K .......... .......... .......... .......... .......... 25%  180M 2s
 13700K .......... .......... .......... .......... .......... 25% 19,5M 2s
 13750K .......... .......... .......... .......... .......... 25% 18,3M 2s
 13800K .......... .......... .......... .......... .......... 25% 70,3M 2s
 13850K .......... .......... .......... .......... .......... 25% 16,2M 2s
 13900K .......... .......... .......... .......... .......... 25% 18,9M 2s
 13950K .......... .......... .......... .......... .......... 25%  141M 2s
 14000K .......... .......... .......... .......... .......... 26%  134M 2s
 14050K .......... .......... .......... .......... .......... 26% 19,0M 2s
 14100K .......... .......... .......... .......... .......... 26% 90,8M 2s
 14150K .......... .......... .......... .......... .......... 26% 18,6M 2s
 14200K .......... .......... .......... .......... .......... 26% 83,5M 2s
 14250K .......... .......... .......... .......... .......... 26% 22,4M 2s
 14300K .......... .......... .......... .......... .......... 26% 48,6M 2s
 14350K .......... .......... .......... .......... .......... 26% 23,4M 2s
 14400K .......... .......... .......... .......... .......... 26% 68,8M 2s
 14450K .......... .......... .......... .......... .......... 26% 15,3M 2s
 14500K .......... .......... .......... .......... .......... 26%  167M 2s
 14550K .......... .......... .......... .......... .......... 27% 15,9M 2s
 14600K .......... .......... .......... .......... .......... 27%  168M 2s
 14650K .......... .......... .......... .......... .......... 27% 18,0M 2s
 14700K .......... .......... .......... .......... .......... 27%  160M 2s
 14750K .......... .......... .......... .......... .......... 27% 15,3M 2s
 14800K .......... .......... .......... .......... .......... 27% 66,4M 2s
 14850K .......... .......... .......... .......... .......... 27% 24,8M 2s
 14900K .......... .......... .......... .......... .......... 27% 24,3M 2s
 14950K .......... .......... .......... .......... .......... 27%  115M 2s
 15000K .......... .......... .......... .......... .......... 27% 19,7M 2s
 15050K .......... .......... .......... .......... .......... 28% 72,0M 2s
 15100K .......... .......... .......... .......... .......... 28% 13,8M 2s
 15150K .......... .......... .......... .......... .......... 28%  110M 2s
 15200K .......... .......... .......... .......... .......... 28% 38,9M 2s
 15250K .......... .......... .......... .......... .......... 28% 54,0M 2s
 15300K .......... .......... .......... .......... .......... 28% 21,5M 2s
 15350K .......... .......... .......... .......... .......... 28% 63,9M 2s
 15400K .......... .......... .......... .......... .......... 28% 19,8M 2s
 15450K .......... .......... .......... .......... .......... 28% 77,4M 2s
 15500K .......... .......... .......... .......... .......... 28% 15,9M 2s
 15550K .......... .......... .......... .......... .......... 28%  107M 2s
 15600K .......... .......... .......... .......... .......... 29% 11,6M 2s
 15650K .......... .......... .......... .......... .......... 29% 75,1M 2s
 15700K .......... .......... .......... .......... .......... 29%  163M 2s
 15750K .......... .......... .......... .......... .......... 29% 16,1M 2s
 15800K .......... .......... .......... .......... .......... 29%  101M 2s
 15850K .......... .......... .......... .......... .......... 29% 16,7M 2s
 15900K .......... .......... .......... .......... .......... 29% 84,8M 2s
 15950K .......... .......... .......... .......... .......... 29% 17,9M 2s
 16000K .......... .......... .......... .......... .......... 29% 84,5M 2s
 16050K .......... .......... .......... .......... .......... 29% 23,1M 2s
 16100K .......... .......... .......... .......... .......... 29% 79,0M 2s
 16150K .......... .......... .......... .......... .......... 30% 19,3M 2s
 16200K .......... .......... .......... .......... .......... 30% 60,4M 2s
 16250K .......... .......... .......... .......... .......... 30% 18,9M 2s
 16300K .......... .......... .......... .......... .......... 30% 99,9M 2s
 16350K .......... .......... .......... .......... .......... 30% 11,8M 2s
 16400K .......... .......... .......... .......... .......... 30% 85,4M 2s
 16450K .......... .......... .......... .......... .......... 30% 63,2M 2s
 16500K .......... .......... .......... .......... .......... 30% 19,3M 2s
 16550K .......... .......... .......... .......... .......... 30% 81,8M 2s
 16600K .......... .......... .......... .......... .......... 30% 12,3M 2s
 16650K .......... .......... .......... .......... .......... 30% 95,4M 2s
 16700K .......... .......... .......... .......... .......... 31% 24,4M 2s
 16750K .......... .......... .......... .......... .......... 31% 78,4M 2s
 16800K .......... .......... .......... .......... .......... 31% 20,7M 1s
 16850K .......... .......... .......... .......... .......... 31% 47,2M 1s
 16900K .......... .......... .......... .......... .......... 31%  172M 1s
 16950K .......... .......... .......... .......... .......... 31% 19,1M 1s
 17000K .......... .......... .......... .......... .......... 31% 21,4M 1s
 17050K .......... .......... .......... .......... .......... 31% 44,0M 1s
 17100K .......... .......... .......... .......... .......... 31% 25,4M 1s
 17150K .......... .......... .......... .......... .......... 31% 74,9M 1s
 17200K .......... .......... .......... .......... .......... 31% 25,1M 1s
 17250K .......... .......... .......... .......... .......... 32% 86,4M 1s
 17300K .......... .......... .......... .......... .......... 32% 18,2M 1s
 17350K .......... .......... .......... .......... .......... 32% 57,9M 1s
 17400K .......... .......... .......... .......... .......... 32% 21,1M 1s
 17450K .......... .......... .......... .......... .......... 32%  101M 1s
 17500K .......... .......... .......... .......... .......... 32% 16,0M 1s
 17550K .......... .......... .......... .......... .......... 32% 22,5M 1s
 17600K .......... .......... .......... .......... .......... 32% 54,6M 1s
 17650K .......... .......... .......... .......... .......... 32% 18,2M 1s
 17700K .......... .......... .......... .......... .......... 32% 95,4M 1s
 17750K .......... .......... .......... .......... .......... 33% 22,4M 1s
 17800K .......... .......... .......... .......... .......... 33% 60,8M 1s
 17850K .......... .......... .......... .......... .......... 33% 21,5M 1s
 17900K .......... .......... .......... .......... .......... 33% 82,1M 1s
 17950K .......... .......... .......... .......... .......... 33% 13,0M 1s
 18000K .......... .......... .......... .......... .......... 33%  109M 1s
 18050K .......... .......... .......... .......... .......... 33% 20,6M 1s
 18100K .......... .......... .......... .......... .......... 33% 73,2M 1s
 18150K .......... .......... .......... .......... .......... 33% 24,2M 1s
 18200K .......... .......... .......... .......... .......... 33%  120M 1s
 18250K .......... .......... .......... .......... .......... 33% 16,9M 1s
 18300K .......... .......... .......... .......... .......... 34% 15,3M 1s
 18350K .......... .......... .......... .......... .......... 34%  116M 1s
 18400K .......... .......... .......... .......... .......... 34% 18,3M 1s
 18450K .......... .......... .......... .......... .......... 34% 51,3M 1s
 18500K .......... .......... .......... .......... .......... 34% 23,1M 1s
 18550K .......... .......... .......... .......... .......... 34%  125M 1s
 18600K .......... .......... .......... .......... .......... 34% 17,2M 1s
 18650K .......... .......... .......... .......... .......... 34% 69,0M 1s
 18700K .......... .......... .......... .......... .......... 34% 20,0M 1s
 18750K .......... .......... .......... .......... .......... 34% 72,9M 1s
 18800K .......... .......... .......... .......... .......... 34% 22,3M 1s
 18850K .......... .......... .......... .......... .......... 35% 50,0M 1s
 18900K .......... .......... .......... .......... .......... 35% 19,9M 1s
 18950K .......... .......... .......... .......... .......... 35%  109M 1s
 19000K .......... .......... .......... .......... .......... 35% 21,1M 1s
 19050K .......... .......... .......... .......... .......... 35% 45,4M 1s
 19100K .......... .......... .......... .......... .......... 35% 19,2M 1s
 19150K .......... .......... .......... .......... .......... 35% 66,7M 1s
 19200K .......... .......... .......... .......... .......... 35% 26,6M 1s
 19250K .......... .......... .......... .......... .......... 35% 72,6M 1s
 19300K .......... .......... .......... .......... .......... 35% 20,8M 1s
 19350K .......... .......... .......... .......... .......... 35% 49,4M 1s
 19400K .......... .......... .......... .......... .......... 36% 17,1M 1s
 19450K .......... .......... .......... .......... .......... 36% 37,7M 1s
 19500K .......... .......... .......... .......... .......... 36% 13,4M 1s
 19550K .......... .......... .......... .......... .......... 36%  151M 1s
 19600K .......... .......... .......... .......... .......... 36% 43,3M 1s
 19650K .......... .......... .......... .......... .......... 36%  143M 1s
 19700K .......... .......... .......... .......... .......... 36% 17,6M 1s
 19750K .......... .......... .......... .......... .......... 36% 55,4M 1s
 19800K .......... .......... .......... .......... .......... 36% 20,7M 1s
 19850K .......... .......... .......... .......... .......... 36% 58,8M 1s
 19900K .......... .......... .......... .......... .......... 37% 19,4M 1s
 19950K .......... .......... .......... .......... .......... 37% 86,9M 1s
 20000K .......... .......... .......... .......... .......... 37% 28,1M 1s
 20050K .......... .......... .......... .......... .......... 37% 13,7M 1s
 20100K .......... .......... .......... .......... .......... 37% 56,4M 1s
 20150K .......... .......... .......... .......... .......... 37% 17,3M 1s
 20200K .......... .......... .......... .......... .......... 37%  130M 1s
 20250K .......... .......... .......... .......... .......... 37% 32,8M 1s
 20300K .......... .......... .......... .......... .......... 37% 51,7M 1s
 20350K .......... .......... .......... .......... .......... 37% 21,0M 1s
 20400K .......... .......... .......... .......... .......... 37% 85,1M 1s
 20450K .......... .......... .......... .......... .......... 38% 19,2M 1s
 20500K .......... .......... .......... .......... .......... 38% 97,3M 1s
 20550K .......... .......... .......... .......... .......... 38% 15,5M 1s
 20600K .......... .......... .......... .......... .......... 38% 59,0M 1s
 20650K .......... .......... .......... .......... .......... 38% 26,7M 1s
 20700K .......... .......... .......... .......... .......... 38% 52,8M 1s
 20750K .......... .......... .......... .......... .......... 38% 17,9M 1s
 20800K .......... .......... .......... .......... .......... 38% 15,5M 1s
 20850K .......... .......... .......... .......... .......... 38%  108M 1s
 20900K .......... .......... .......... .......... .......... 38% 16,3M 1s
 20950K .......... .......... .......... .......... .......... 38% 67,5M 1s
 21000K .......... .......... .......... .......... .......... 39% 50,0M 1s
 21050K .......... .......... .......... .......... .......... 39% 49,2M 1s
 21100K .......... .......... .......... .......... .......... 39% 12,9M 1s
 21150K .......... .......... .......... .......... .......... 39% 64,2M 1s
 21200K .......... .......... .......... .......... .......... 39% 50,2M 1s
 21250K .......... .......... .......... .......... .......... 39% 85,5M 1s
 21300K .......... .......... .......... .......... .......... 39% 18,0M 1s
 21350K .......... .......... .......... .......... .......... 39% 27,5M 1s
 21400K .......... .......... .......... .......... .......... 39% 29,2M 1s
 21450K .......... .......... .......... .......... .......... 39%  144M 1s
 21500K .......... .......... .......... .......... .......... 39% 16,4M 1s
 21550K .......... .......... .......... .......... .......... 40% 21,8M 1s
 21600K .......... .......... .......... .......... .......... 40% 68,2M 1s
 21650K .......... .......... .......... .......... .......... 40% 86,3M 1s
 21700K .......... .......... .......... .......... .......... 40% 22,9M 1s
 21750K .......... .......... .......... .......... .......... 40% 47,5M 1s
 21800K .......... .......... .......... .......... .......... 40% 18,7M 1s
 21850K .......... .......... .......... .......... .......... 40% 64,8M 1s
 21900K .......... .......... .......... .......... .......... 40% 21,8M 1s
 21950K .......... .......... .......... .......... .......... 40% 16,8M 1s
 22000K .......... .......... .......... .......... .......... 40% 85,2M 1s
 22050K .......... .......... .......... .......... .......... 40% 20,3M 1s
 22100K .......... .......... .......... .......... .......... 41%  126M 1s
 22150K .......... .......... .......... .......... .......... 41% 17,0M 1s
 22200K .......... .......... .......... .......... .......... 41% 95,4M 1s
 22250K .......... .......... .......... .......... .......... 41% 25,8M 1s
 22300K .......... .......... .......... .......... .......... 41%  100M 1s
 22350K .......... .......... .......... .......... .......... 41% 15,5M 1s
 22400K .......... .......... .......... .......... .......... 41%  146M 1s
 22450K .......... .......... .......... .......... .......... 41% 28,3M 1s
 22500K .......... .......... .......... .......... .......... 41% 45,5M 1s
 22550K .......... .......... .......... .......... .......... 41% 21,5M 1s
 22600K .......... .......... .......... .......... .......... 42% 74,3M 1s
 22650K .......... .......... .......... .......... .......... 42% 10,0M 1s
 22700K .......... .......... .......... .......... .......... 42% 62,5M 1s
 22750K .......... .......... .......... .......... .......... 42% 68,2M 1s
 22800K .......... .......... .......... .......... .......... 42% 18,1M 1s
 22850K .......... .......... .......... .......... .......... 42% 93,0M 1s
 22900K .......... .......... .......... .......... .......... 42% 25,5M 1s
 22950K .......... .......... .......... .......... .......... 42% 93,2M 1s
 23000K .......... .......... .......... .......... .......... 42% 21,4M 1s
 23050K .......... .......... .......... .......... .......... 42% 61,7M 1s
 23100K .......... .......... .......... .......... .......... 42% 12,4M 1s
 23150K .......... .......... .......... .......... .......... 43%  101M 1s
 23200K .......... .......... .......... .......... .......... 43% 43,2M 1s
 23250K .......... .......... .......... .......... .......... 43% 49,3M 1s
 23300K .......... .......... .......... .......... .......... 43% 17,3M 1s
 23350K .......... .......... .......... .......... .......... 43%  145M 1s
 23400K .......... .......... .......... .......... .......... 43% 24,3M 1s
 23450K .......... .......... .......... .......... .......... 43% 50,2M 1s
 23500K .......... .......... .......... .......... .......... 43% 15,9M 1s
 23550K .......... .......... .......... .......... .......... 43% 19,4M 1s
 23600K .......... .......... .......... .......... .......... 43% 57,4M 1s
 23650K .......... .......... .......... .......... .......... 43% 17,7M 1s
 23700K .......... .......... .......... .......... .......... 44%  148M 1s
 23750K .......... .......... .......... .......... .......... 44% 19,6M 1s
 23800K .......... .......... .......... .......... .......... 44% 59,4M 1s
 23850K .......... .......... .......... .......... .......... 44% 15,7M 1s
 23900K .......... .......... .......... .......... .......... 44%  146M 1s
 23950K .......... .......... .......... .......... .......... 44% 29,2M 1s
 24000K .......... .......... .......... .......... .......... 44% 50,4M 1s
 24050K .......... .......... .......... .......... .......... 44% 21,3M 1s
 24100K .......... .......... .......... .......... .......... 44% 88,1M 1s
 24150K .......... .......... .......... .......... .......... 44% 18,4M 1s
 24200K .......... .......... .......... .......... .......... 44% 97,3M 1s
 24250K .......... .......... .......... .......... .......... 45% 11,2M 1s
 24300K .......... .......... .......... .......... .......... 45% 28,8M 1s
 24350K .......... .......... .......... .......... .......... 45% 85,2M 1s
 24400K .......... .......... .......... .......... .......... 45%  162M 1s
 24450K .......... .......... .......... .......... .......... 45% 22,5M 1s
 24500K .......... .......... .......... .......... .......... 45% 79,9M 1s
 24550K .......... .......... .......... .......... .......... 45% 12,2M 1s
 24600K .......... .......... .......... .......... .......... 45%  123M 1s
 24650K .......... .......... .......... .......... .......... 45% 25,0M 1s
 24700K .......... .......... .......... .......... .......... 45%  146M 1s
 24750K .......... .......... .......... .......... .......... 46% 19,8M 1s
 24800K .......... .......... .......... .......... .......... 46% 40,2M 1s
 24850K .......... .......... .......... .......... .......... 46% 19,5M 1s
 24900K .......... .......... .......... .......... .......... 46%  124M 1s
 24950K .......... .......... .......... .......... .......... 46% 21,9M 1s
 25000K .......... .......... .......... .......... .......... 46%  131M 1s
 25050K .......... .......... .......... .......... .......... 46% 23,3M 1s
 25100K .......... .......... .......... .......... .......... 46% 14,0M 1s
 25150K .......... .......... .......... .......... .......... 46%  135M 1s
 25200K .......... .......... .......... .......... .......... 46% 20,1M 1s
 25250K .......... .......... .......... .......... .......... 46% 62,2M 1s
 25300K .......... .......... .......... .......... .......... 47% 22,6M 1s
 25350K .......... .......... .......... .......... .......... 47% 77,6M 1s
 25400K .......... .......... .......... .......... .......... 47% 18,4M 1s
 25450K .......... .......... .......... .......... .......... 47% 9,80M 1s
 25500K .......... .......... .......... .......... .......... 47%  121M 1s
 25550K .......... .......... .......... .......... .......... 47%  109M 1s
 25600K .......... .......... .......... .......... .......... 47% 71,4M 1s
 25650K .......... .......... .......... .......... .......... 47% 47,5M 1s
 25700K .......... .......... .......... .......... .......... 47% 18,4M 1s
 25750K .......... .......... .......... .......... .......... 47% 46,7M 1s
 25800K .......... .......... .......... .......... .......... 47% 16,3M 1s
 25850K .......... .......... .......... .......... .......... 48%  110M 1s
 25900K .......... .......... .......... .......... .......... 48% 19,3M 1s
 25950K .......... .......... .......... .......... .......... 48%  113M 1s
 26000K .......... .......... .......... .......... .......... 48% 25,9M 1s
 26050K .......... .......... .......... .......... .......... 48% 64,3M 1s
 26100K .......... .......... .......... .......... .......... 48% 16,2M 1s
 26150K .......... .......... .......... .......... .......... 48% 65,1M 1s
 26200K .......... .......... .......... .......... .......... 48% 14,7M 1s
 26250K .......... .......... .......... .......... .......... 48%  129M 1s
 26300K .......... .......... .......... .......... .......... 48% 26,2M 1s
 26350K .......... .......... .......... .......... .......... 48% 55,3M 1s
 26400K .......... .......... .......... .......... .......... 49% 19,5M 1s
 26450K .......... .......... .......... .......... .......... 49% 66,1M 1s
 26500K .......... .......... .......... .......... .......... 49% 17,2M 1s
 26550K .......... .......... .......... .......... .......... 49%  112M 1s
 26600K .......... .......... .......... .......... .......... 49% 16,4M 1s
 26650K .......... .......... .......... .......... .......... 49%  235M 1s
 26700K .......... .......... .......... .......... .......... 49% 13,6M 1s
 26750K .......... .......... .......... .......... .......... 49%  160M 1s
 26800K .......... .......... .......... .......... .......... 49% 22,7M 1s
 26850K .......... .......... .......... .......... .......... 49%  110M 1s
 26900K .......... .......... .......... .......... .......... 49% 22,7M 1s
 26950K .......... .......... .......... .......... .......... 50%  108M 1s
 27000K .......... .......... .......... .......... .......... 50% 19,6M 1s
 27050K .......... .......... .......... .......... .......... 50%  128M 1s
 27100K .......... .......... .......... .......... .......... 50% 17,6M 1s
 27150K .......... .......... .......... .......... .......... 50% 44,7M 1s
 27200K .......... .......... .......... .......... .......... 50% 17,6M 1s
 27250K .......... .......... .......... .......... .......... 50% 30,6M 1s
 27300K .......... .......... .......... .......... .......... 50% 20,9M 1s
 27350K .......... .......... .......... .......... .......... 50% 21,1M 1s
 27400K .......... .......... .......... .......... .......... 50%  129M 1s
 27450K .......... .......... .......... .......... .......... 51%  143M 1s
 27500K .......... .......... .......... .......... .......... 51% 23,0M 1s
 27550K .......... .......... .......... .......... .......... 51% 80,3M 1s
 27600K .......... .......... .......... .......... .......... 51% 15,6M 1s
 27650K .......... .......... .......... .......... .......... 51% 91,6M 1s
 27700K .......... .......... .......... .......... .......... 51% 17,1M 1s
 27750K .......... .......... .......... .......... .......... 51%  105M 1s
 27800K .......... .......... .......... .......... .......... 51% 16,0M 1s
 27850K .......... .......... .......... .......... .......... 51%  265M 1s
 27900K .......... .......... .......... .......... .......... 51% 19,7M 1s
 27950K .......... .......... .......... .......... .......... 51% 92,1M 1s
 28000K .......... .......... .......... .......... .......... 52% 16,3M 1s
 28050K .......... .......... .......... .......... .......... 52% 18,3M 1s
 28100K .......... .......... .......... .......... .......... 52%  167M 1s
 28150K .......... .......... .......... .......... .......... 52% 20,8M 1s
 28200K .......... .......... .......... .......... .......... 52% 66,3M 1s
 28250K .......... .......... .......... .......... .......... 52% 20,8M 1s
 28300K .......... .......... .......... .......... .......... 52%  103M 1s
 28350K .......... .......... .......... .......... .......... 52% 16,3M 1s
 28400K .......... .......... .......... .......... .......... 52% 54,6M 1s
 28450K .......... .......... .......... .......... .......... 52% 36,1M 1s
 28500K .......... .......... .......... .......... .......... 52% 44,8M 1s
 28550K .......... .......... .......... .......... .......... 53% 14,7M 1s
 28600K .......... .......... .......... .......... .......... 53%  114M 1s
 28650K .......... .......... .......... .......... .......... 53% 15,7M 1s
 28700K .......... .......... .......... .......... .......... 53%  102M 1s
 28750K .......... .......... .......... .......... .......... 53% 27,3M 1s
 28800K .......... .......... .......... .......... .......... 53% 56,7M 1s
 28850K .......... .......... .......... .......... .......... 53% 15,2M 1s
 28900K .......... .......... .......... .......... .......... 53% 81,1M 1s
 28950K .......... .......... .......... .......... .......... 53% 19,9M 1s
 29000K .......... .......... .......... .......... .......... 53%  182M 1s
 29050K .......... .......... .......... .......... .......... 53% 29,0M 1s
 29100K .......... .......... .......... .......... .......... 54% 17,0M 1s
 29150K .......... .......... .......... .......... .......... 54% 90,9M 1s
 29200K .......... .......... .......... .......... .......... 54% 21,1M 1s
 29250K .......... .......... .......... .......... .......... 54% 52,8M 1s
 29300K .......... .......... .......... .......... .......... 54% 17,2M 1s
 29350K .......... .......... .......... .......... .......... 54% 72,2M 1s
 29400K .......... .......... .......... .......... .......... 54% 29,3M 1s
 29450K .......... .......... .......... .......... .......... 54% 83,2M 1s
 29500K .......... .......... .......... .......... .......... 54% 23,9M 1s
 29550K .......... .......... .......... .......... .......... 54% 53,5M 1s
 29600K .......... .......... .......... .......... .......... 54% 14,4M 1s
 29650K .......... .......... .......... .......... .......... 55%  242M 1s
 29700K .......... .......... .......... .......... .......... 55% 22,0M 1s
 29750K .......... .......... .......... .......... .......... 55%  106M 1s
 29800K .......... .......... .......... .......... .......... 55% 16,3M 1s
 29850K .......... .......... .......... .......... .......... 55% 68,3M 1s
 29900K .......... .......... .......... .......... .......... 55% 16,2M 1s
 29950K .......... .......... .......... .......... .......... 55% 17,0M 1s
 30000K .......... .......... .......... .......... .......... 55%  111M 1s
 30050K .......... .......... .......... .......... .......... 55% 19,3M 1s
 30100K .......... .......... .......... .......... .......... 55% 57,6M 1s
 30150K .......... .......... .......... .......... .......... 56% 24,2M 1s
 30200K .......... .......... .......... .......... .......... 56%  254M 1s
 30250K .......... .......... .......... .......... .......... 56% 22,7M 1s
 30300K .......... .......... .......... .......... .......... 56% 55,9M 1s
 30350K .......... .......... .......... .......... .......... 56% 15,1M 1s
 30400K .......... .......... .......... .......... .......... 56% 93,2M 1s
 30450K .......... .......... .......... .......... .......... 56% 13,6M 1s
 30500K .......... .......... .......... .......... .......... 56% 59,6M 1s
 30550K .......... .......... .......... .......... .......... 56% 32,4M 1s
 30600K .......... .......... .......... .......... .......... 56% 82,6M 1s
 30650K .......... .......... .......... .......... .......... 56% 17,1M 1s
 30700K .......... .......... .......... .......... .......... 57% 74,8M 1s
 30750K .......... .......... .......... .......... .......... 57% 28,9M 1s
 30800K .......... .......... .......... .......... .......... 57%  108M 1s
 30850K .......... .......... .......... .......... .......... 57% 16,3M 1s
 30900K .......... .......... .......... .......... .......... 57%  127M 1s
 30950K .......... .......... .......... .......... .......... 57% 21,0M 1s
 31000K .......... .......... .......... .......... .......... 57% 13,1M 1s
 31050K .......... .......... .......... .......... .......... 57%  155M 1s
 31100K .......... .......... .......... .......... .......... 57% 26,2M 1s
 31150K .......... .......... .......... .......... .......... 57%  131M 1s
 31200K .......... .......... .......... .......... .......... 57% 16,1M 1s
 31250K .......... .......... .......... .......... .......... 58% 69,2M 1s
 31300K .......... .......... .......... .......... .......... 58% 20,9M 1s
 31350K .......... .......... .......... .......... .......... 58% 29,0M 1s
 31400K .......... .......... .......... .......... .......... 58% 27,1M 1s
 31450K .......... .......... .......... .......... .......... 58%  185M 1s
 31500K .......... .......... .......... .......... .......... 58% 14,9M 1s
 31550K .......... .......... .......... .......... .......... 58%  170M 1s
 31600K .......... .......... .......... .......... .......... 58% 23,9M 1s
 31650K .......... .......... .......... .......... .......... 58% 94,6M 1s
 31700K .......... .......... .......... .......... .......... 58% 17,9M 1s
 31750K .......... .......... .......... .......... .......... 58% 98,8M 1s
 31800K .......... .......... .......... .......... .......... 59% 18,2M 1s
 31850K .......... .......... .......... .......... .......... 59% 23,1M 1s
 31900K .......... .......... .......... .......... .......... 59% 52,7M 1s
 31950K .......... .......... .......... .......... .......... 59% 14,8M 1s
 32000K .......... .......... .......... .......... .......... 59%  158M 1s
 32050K .......... .......... .......... .......... .......... 59% 25,1M 1s
 32100K .......... .......... .......... .......... .......... 59% 70,6M 1s
 32150K .......... .......... .......... .......... .......... 59% 23,3M 1s
 32200K .......... .......... .......... .......... .......... 59% 49,8M 1s
 32250K .......... .......... .......... .......... .......... 59% 19,2M 1s
 32300K .......... .......... .......... .......... .......... 60% 75,4M 1s
 32350K .......... .......... .......... .......... .......... 60% 14,5M 1s
 32400K .......... .......... .......... .......... .......... 60%  185M 1s
 32450K .......... .......... .......... .......... .......... 60% 24,8M 1s
 32500K .......... .......... .......... .......... .......... 60%  150M 1s
 32550K .......... .......... .......... .......... .......... 60% 15,6M 1s
 32600K .......... .......... .......... .......... .......... 60%  168M 1s
 32650K .......... .......... .......... .......... .......... 60% 17,0M 1s
 32700K .......... .......... .......... .......... .......... 60% 90,1M 1s
 32750K .......... .......... .......... .......... .......... 60% 7,01M 1s
 32800K .......... .......... .......... .......... .......... 60% 30,7M 1s
 32850K .......... .......... .......... .......... .......... 61%  208M 1s
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 32950K .......... .......... .......... .......... .......... 61%  191M 1s
 33000K .......... .......... .......... .......... .......... 61% 86,7M 1s
 33050K .......... .......... .......... .......... .......... 61% 16,6M 1s
 33100K .......... .......... .......... .......... .......... 61%  135M 1s
 33150K .......... .......... .......... .......... .......... 61% 16,7M 1s
 33200K .......... .......... .......... .......... .......... 61% 19,8M 1s
 33250K .......... .......... .......... .......... .......... 61% 92,8M 1s
 33300K .......... .......... .......... .......... .......... 61% 19,6M 1s
 33350K .......... .......... .......... .......... .......... 61% 76,5M 1s
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 33500K .......... .......... .......... .......... .......... 62% 18,5M 1s
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 33800K .......... .......... .......... .......... .......... 62% 16,8M 1s
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 34050K .......... .......... .......... .......... .......... 63% 29,3M 1s
 34100K .......... .......... .......... .......... .......... 63% 64,8M 1s
 34150K .......... .......... .......... .......... .......... 63% 21,5M 1s
 34200K .......... .......... .......... .......... .......... 63% 53,9M 1s
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 34800K .......... .......... .......... .......... .......... 64% 20,2M 1s
 34850K .......... .......... .......... .......... .......... 64% 80,3M 1s
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 35100K .......... .......... .......... .......... .......... 65% 25,0M 1s
 35150K .......... .......... .......... .......... .......... 65%  129M 1s
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 35300K .......... .......... .......... .......... .......... 65% 75,2M 1s
 35350K .......... .......... .......... .......... .......... 65%  171M 1s
 35400K .......... .......... .......... .......... .......... 65% 13,8M 1s
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 36100K .......... .......... .......... .......... .......... 67% 56,3M 1s
 36150K .......... .......... .......... .......... .......... 67% 14,1M 1s
 36200K .......... .......... .......... .......... .......... 67%  107M 1s
 36250K .......... .......... .......... .......... .......... 67% 30,7M 1s
 36300K .......... .......... .......... .......... .......... 67% 51,1M 1s
 36350K .......... .......... .......... .......... .......... 67% 38,3M 1s
 36400K .......... .......... .......... .......... .......... 67% 14,8M 1s
 36450K .......... .......... .......... .......... .......... 67% 75,6M 1s
 36500K .......... .......... .......... .......... .......... 67% 13,6M 1s
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 36600K .......... .......... .......... .......... .......... 67% 17,8M 1s
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 36700K .......... .......... .......... .......... .......... 68% 23,0M 1s
 36750K .......... .......... .......... .......... .......... 68% 91,4M 1s
 36800K .......... .......... .......... .......... .......... 68% 9,21M 1s
 36850K .......... .......... .......... .......... .......... 68% 48,7M 1s
 36900K .......... .......... .......... .......... .......... 68% 93,5M 1s
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 37000K .......... .......... .......... .......... .......... 68% 39,8M 1s
 37050K .......... .......... .......... .......... .......... 68% 51,8M 1s
 37100K .......... .......... .......... .......... .......... 68% 17,0M 1s
 37150K .......... .......... .......... .......... .......... 69% 26,7M 1s
 37200K .......... .......... .......... .......... .......... 69%  135M 1s
 37250K .......... .......... .......... .......... .......... 69% 21,6M 1s
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 37700K .......... .......... .......... .......... .......... 70% 60,5M 1s
 37750K .......... .......... .......... .......... .......... 70% 22,2M 1s
 37800K .......... .......... .......... .......... .......... 70% 36,2M 1s
 37850K .......... .......... .......... .......... .......... 70% 20,6M 1s
 37900K .......... .......... .......... .......... .......... 70%  125M 1s
 37950K .......... .......... .......... .......... .......... 70% 29,5M 1s
 38000K .......... .......... .......... .......... .......... 70% 80,7M 1s
 38050K .......... .......... .......... .......... .......... 70% 17,1M 1s
 38100K .......... .......... .......... .......... .......... 70% 58,7M 1s
 38150K .......... .......... .......... .......... .......... 70% 27,5M 1s
 38200K .......... .......... .......... .......... .......... 70% 43,1M 1s
 38250K .......... .......... .......... .......... .......... 71% 13,0M 1s
 38300K .......... .......... .......... .......... .......... 71% 25,0M 1s
 38350K .......... .......... .......... .......... .......... 71% 25,7M 1s
 38400K .......... .......... .......... .......... .......... 71% 58,8M 1s
 38450K .......... .......... .......... .......... .......... 71%  136M 1s
 38500K .......... .......... .......... .......... .......... 71% 18,6M 1s
 38550K .......... .......... .......... .......... .......... 71% 52,3M 1s
 38600K .......... .......... .......... .......... .......... 71% 14,9M 1s
 38650K .......... .......... .......... .......... .......... 71%  147M 1s
 38700K .......... .......... .......... .......... .......... 71% 18,8M 1s
 38750K .......... .......... .......... .......... .......... 71% 61,9M 1s
 38800K .......... .......... .......... .......... .......... 72% 21,1M 1s
 38850K .......... .......... .......... .......... .......... 72% 59,4M 1s
 38900K .......... .......... .......... .......... .......... 72% 20,1M 1s
 38950K .......... .......... .......... .......... .......... 72% 61,4M 1s
 39000K .......... .......... .......... .......... .......... 72% 15,6M 1s
 39050K .......... .......... .......... .......... .......... 72%  135M 1s
 39100K .......... .......... .......... .......... .......... 72% 29,2M 1s
 39150K .......... .......... .......... .......... .......... 72% 63,7M 1s
 39200K .......... .......... .......... .......... .......... 72% 16,3M 1s
 39250K .......... .......... .......... .......... .......... 72%  126M 1s
 39300K .......... .......... .......... .......... .......... 72% 24,4M 1s
 39350K .......... .......... .......... .......... .......... 73% 37,5M 1s
 39400K .......... .......... .......... .......... .......... 73% 17,5M 1s
 39450K .......... .......... .......... .......... .......... 73%  198M 1s
 39500K .......... .......... .......... .......... .......... 73% 20,4M 1s
 39550K .......... .......... .......... .......... .......... 73%  148M 1s
 39600K .......... .......... .......... .......... .......... 73% 16,9M 1s
 39650K .......... .......... .......... .......... .......... 73% 76,8M 1s
 39700K .......... .......... .......... .......... .......... 73% 17,8M 1s
 39750K .......... .......... .......... .......... .......... 73%  164M 1s
 39800K .......... .......... .......... .......... .......... 73% 23,1M 1s
 39850K .......... .......... .......... .......... .......... 74% 92,3M 1s
 39900K .......... .......... .......... .......... .......... 74% 12,9M 1s
 39950K .......... .......... .......... .......... .......... 74% 22,9M 0s
 40000K .......... .......... .......... .......... .......... 74%  220M 0s
 40050K .......... .......... .......... .......... .......... 74% 16,1M 0s
 40100K .......... .......... .......... .......... .......... 74% 76,5M 0s
 40150K .......... .......... .......... .......... .......... 74% 27,7M 0s
 40200K .......... .......... .......... .......... .......... 74% 55,9M 0s
 40250K .......... .......... .......... .......... .......... 74% 17,6M 0s
 40300K .......... .......... .......... .......... .......... 74%  225M 0s
 40350K .......... .......... .......... .......... .......... 74% 24,7M 0s
 40400K .......... .......... .......... .......... .......... 75% 61,7M 0s
 40450K .......... .......... .......... .......... .......... 75% 21,9M 0s
 40500K .......... .......... .......... .......... .......... 75% 49,2M 0s
 40550K .......... .......... .......... .......... .......... 75% 20,0M 0s
 40600K .......... .......... .......... .......... .......... 75% 75,8M 0s
 40650K .......... .......... .......... .......... .......... 75% 17,0M 0s
 40700K .......... .......... .......... .......... .......... 75%  187M 0s
 40750K .......... .......... .......... .......... .......... 75% 16,3M 0s
 40800K .......... .......... .......... .......... .......... 75% 25,0M 0s
 40850K .......... .......... .......... .......... .......... 75% 48,8M 0s
 40900K .......... .......... .......... .......... .......... 75% 22,2M 0s
 40950K .......... .......... .......... .......... .......... 76% 56,1M 0s
 41000K .......... .......... .......... .......... .......... 76% 20,0M 0s
 41050K .......... .......... .......... .......... .......... 76% 15,9M 0s
 41100K .......... .......... .......... .......... .......... 76% 81,5M 0s
 41150K .......... .......... .......... .......... .......... 76% 35,8M 0s
 41200K .......... .......... .......... .......... .......... 76% 20,9M 0s
 41250K .......... .......... .......... .......... .......... 76%  167M 0s
 41300K .......... .......... .......... .......... .......... 76% 18,7M 0s
 41350K .......... .......... .......... .......... .......... 76%  155M 0s
 41400K .......... .......... .......... .......... .......... 76% 26,0M 0s
 41450K .......... .......... .......... .......... .......... 76% 81,5M 0s
 41500K .......... .......... .......... .......... .......... 77% 13,4M 0s
 41550K .......... .......... .......... .......... .......... 77% 20,1M 0s
 41600K .......... .......... .......... .......... .......... 77%  114M 0s
 41650K .......... .......... .......... .......... .......... 77% 14,7M 0s
 41700K .......... .......... .......... .......... .......... 77%  146M 0s
 41750K .......... .......... .......... .......... .......... 77% 32,8M 0s
 41800K .......... .......... .......... .......... .......... 77% 98,6M 0s
 41850K .......... .......... .......... .......... .......... 77% 15,9M 0s
 41900K .......... .......... .......... .......... .......... 77%  144M 0s
 41950K .......... .......... .......... .......... .......... 77% 20,5M 0s
 42000K .......... .......... .......... .......... .......... 78% 70,1M 0s
 42050K .......... .......... .......... .......... .......... 78% 15,9M 0s
 42100K .......... .......... .......... .......... .......... 78%  227M 0s
 42150K .......... .......... .......... .......... .......... 78% 28,2M 0s
 42200K .......... .......... .......... .......... .......... 78% 62,4M 0s
 42250K .......... .......... .......... .......... .......... 78% 19,4M 0s
 42300K .......... .......... .......... .......... .......... 78% 93,9M 0s
 42350K .......... .......... .......... .......... .......... 78% 13,6M 0s
 42400K .......... .......... .......... .......... .......... 78%  103M 0s
 42450K .......... .......... .......... .......... .......... 78% 21,0M 0s
 42500K .......... .......... .......... .......... .......... 78% 43,2M 0s
 42550K .......... .......... .......... .......... .......... 79% 17,7M 0s
 42600K .......... .......... .......... .......... .......... 79%  102M 0s
 42650K .......... .......... .......... .......... .......... 79% 22,3M 0s
 42700K .......... .......... .......... .......... .......... 79%  116M 0s
 42750K .......... .......... .......... .......... .......... 79% 18,3M 0s
 42800K .......... .......... .......... .......... .......... 79% 15,8M 0s
 42850K .......... .......... .......... .......... .......... 79%  103M 0s
 42900K .......... .......... .......... .......... .......... 79% 16,4M 0s
 42950K .......... .......... .......... .......... .......... 79% 93,7M 0s
 43000K .......... .......... .......... .......... .......... 79% 15,4M 0s
 43050K .......... .......... .......... .......... .......... 79%  263M 0s
 43100K .......... .......... .......... .......... .......... 80% 26,9M 0s
 43150K .......... .......... .......... .......... .......... 80% 55,4M 0s
 43200K .......... .......... .......... .......... .......... 80% 20,0M 0s
 43250K .......... .......... .......... .......... .......... 80% 98,8M 0s
 43300K .......... .......... .......... .......... .......... 80% 14,8M 0s
 43350K .......... .......... .......... .......... .......... 80%  208M 0s
 43400K .......... .......... .......... .......... .......... 80% 21,0M 0s
 43450K .......... .......... .......... .......... .......... 80% 19,0M 0s
 43500K .......... .......... .......... .......... .......... 80%  103M 0s
 43550K .......... .......... .......... .......... .......... 80% 23,5M 0s
 43600K .......... .......... .......... .......... .......... 80%  108M 0s
 43650K .......... .......... .......... .......... .......... 81% 14,8M 0s
 43700K .......... .......... .......... .......... .......... 81% 93,0M 0s
 43750K .......... .......... .......... .......... .......... 81% 29,8M 0s
 43800K .......... .......... .......... .......... .......... 81% 63,2M 0s
 43850K .......... .......... .......... .......... .......... 81% 17,4M 0s
 43900K .......... .......... .......... .......... .......... 81%  160M 0s
 43950K .......... .......... .......... .......... .......... 81% 23,4M 0s
 44000K .......... .......... .......... .......... .......... 81% 38,0M 0s
 44050K .......... .......... .......... .......... .......... 81% 25,3M 0s
 44100K .......... .......... .......... .......... .......... 81% 13,4M 0s
 44150K .......... .......... .......... .......... .......... 81% 57,2M 0s
 44200K .......... .......... .......... .......... .......... 82% 26,6M 0s
 44250K .......... .......... .......... .......... .......... 82% 26,0M 0s
 44300K .......... .......... .......... .......... .......... 82% 36,7M 0s
 44350K .......... .......... .......... .......... .......... 82% 37,9M 0s
 44400K .......... .......... .......... .......... .......... 82% 28,9M 0s
 44450K .......... .......... .......... .......... .......... 82% 74,3M 0s
 44500K .......... .......... .......... .......... .......... 82% 18,1M 0s
 44550K .......... .......... .......... .......... .......... 82%  151M 0s
 44600K .......... .......... .......... .......... .......... 82% 13,0M 0s
 44650K .......... .......... .......... .......... .......... 82% 47,4M 0s
 44700K .......... .......... .......... .......... .......... 83% 21,9M 0s
 44750K .......... .......... .......... .......... .......... 83%  221M 0s
 44800K .......... .......... .......... .......... .......... 83% 37,9M 0s
 44850K .......... .......... .......... .......... .......... 83% 99,6M 0s
 44900K .......... .......... .......... .......... .......... 83% 20,3M 0s
 44950K .......... .......... .......... .......... .......... 83% 63,9M 0s
 45000K .......... .......... .......... .......... .......... 83% 19,5M 0s
 45050K .......... .......... .......... .......... .......... 83% 41,4M 0s
 45100K .......... .......... .......... .......... .......... 83% 19,6M 0s
 45150K .......... .......... .......... .......... .......... 83% 17,8M 0s
 45200K .......... .......... .......... .......... .......... 83%  198M 0s
 45250K .......... .......... .......... .......... .......... 84% 17,7M 0s
 45300K .......... .......... .......... .......... .......... 84% 88,9M 0s
 45350K .......... .......... .......... .......... .......... 84% 22,8M 0s
 45400K .......... .......... .......... .......... .......... 84% 60,4M 0s
 45450K .......... .......... .......... .......... .......... 84% 13,8M 0s
 45500K .......... .......... .......... .......... .......... 84%  250M 0s
 45550K .......... .......... .......... .......... .......... 84% 20,1M 0s
 45600K .......... .......... .......... .......... .......... 84% 18,7M 0s
 45650K .......... .......... .......... .......... .......... 84%  122M 0s
 45700K .......... .......... .......... .......... .......... 84% 21,5M 0s
 45750K .......... .......... .......... .......... .......... 84% 61,3M 0s
 45800K .......... .......... .......... .......... .......... 85% 22,3M 0s
 45850K .......... .......... .......... .......... .......... 85% 55,5M 0s
 45900K .......... .......... .......... .......... .......... 85% 17,8M 0s
 45950K .......... .......... .......... .......... .......... 85% 30,5M 0s
 46000K .......... .......... .......... .......... .......... 85% 24,0M 0s
 46050K .......... .......... .......... .......... .......... 85% 73,4M 0s
 46100K .......... .......... .......... .......... .......... 85% 21,0M 0s
 46150K .......... .......... .......... .......... .......... 85% 57,5M 0s
 46200K .......... .......... .......... .......... .......... 85% 14,6M 0s
 46250K .......... .......... .......... .......... .......... 85%  154M 0s
 46300K .......... .......... .......... .......... .......... 85% 25,7M 0s
 46350K .......... .......... .......... .......... .......... 86% 78,1M 0s
 46400K .......... .......... .......... .......... .......... 86% 22,1M 0s
 46450K .......... .......... .......... .......... .......... 86% 72,0M 0s
 46500K .......... .......... .......... .......... .......... 86% 18,2M 0s
 46550K .......... .......... .......... .......... .......... 86%  138M 0s
 46600K .......... .......... .......... .......... .......... 86% 26,0M 0s
 46650K .......... .......... .......... .......... .......... 86% 57,0M 0s
 46700K .......... .......... .......... .......... .......... 86% 16,6M 0s
 46750K .......... .......... .......... .......... .......... 86% 64,2M 0s
 46800K .......... .......... .......... .......... .......... 86% 21,5M 0s
 46850K .......... .......... .......... .......... .......... 86% 14,1M 0s
 46900K .......... .......... .......... .......... .......... 87% 82,8M 0s
 46950K .......... .......... .......... .......... .......... 87% 41,9M 0s
 47000K .......... .......... .......... .......... .......... 87% 75,5M 0s
 47050K .......... .......... .......... .......... .......... 87% 16,9M 0s
 47100K .......... .......... .......... .......... .......... 87% 61,7M 0s
 47150K .......... .......... .......... .......... .......... 87% 26,2M 0s
 47200K .......... .......... .......... .......... .......... 87% 39,4M 0s
 47250K .......... .......... .......... .......... .......... 87% 25,9M 0s
 47300K .......... .......... .......... .......... .......... 87% 95,6M 0s
 47350K .......... .......... .......... .......... .......... 87% 19,4M 0s
 47400K .......... .......... .......... .......... .......... 88% 80,3M 0s
 47450K .......... .......... .......... .......... .......... 88% 16,3M 0s
 47500K .......... .......... .......... .......... .......... 88% 59,4M 0s
 47550K .......... .......... .......... .......... .......... 88% 15,0M 0s
 47600K .......... .......... .......... .......... .......... 88%  201M 0s
 47650K .......... .......... .......... .......... .......... 88% 17,0M 0s
 47700K .......... .......... .......... .......... .......... 88%  242M 0s
 47750K .......... .......... .......... .......... .......... 88% 20,6M 0s
 47800K .......... .......... .......... .......... .......... 88% 29,5M 0s
 47850K .......... .......... .......... .......... .......... 88% 22,1M 0s
 47900K .......... .......... .......... .......... .......... 88%  279M 0s
 47950K .......... .......... .......... .......... .......... 89% 24,6M 0s
 48000K .......... .......... .......... .......... .......... 89% 16,6M 0s
 48050K .......... .......... .......... .......... .......... 89%  286M 0s
 48100K .......... .......... .......... .......... .......... 89% 63,4M 0s
 48150K .......... .......... .......... .......... .......... 89% 19,3M 0s
 48200K .......... .......... .......... .......... .......... 89% 21,8M 0s
 48250K .......... .......... .......... .......... .......... 89% 57,7M 0s
 48300K .......... .......... .......... .......... .......... 89% 14,3M 0s
 48350K .......... .......... .......... .......... .......... 89% 98,2M 0s
 48400K .......... .......... .......... .......... .......... 89% 11,7M 0s
 48450K .......... .......... .......... .......... .......... 89%  151M 0s
 48500K .......... .......... .......... .......... .......... 90% 32,9M 0s
 48550K .......... .......... .......... .......... .......... 90%  159M 0s
 48600K .......... .......... .......... .......... .......... 90% 18,9M 0s
 48650K .......... .......... .......... .......... .......... 90%  118M 0s
 48700K .......... .......... .......... .......... .......... 90% 18,7M 0s
 48750K .......... .......... .......... .......... .......... 90% 60,3M 0s
 48800K .......... .......... .......... .......... .......... 90% 19,8M 0s
 48850K .......... .......... .......... .......... .......... 90% 55,0M 0s
 48900K .......... .......... .......... .......... .......... 90% 20,4M 0s
 48950K .......... .......... .......... .......... .......... 90%  128M 0s
 49000K .......... .......... .......... .......... .......... 90% 14,7M 0s
 49050K .......... .......... .......... .......... .......... 91%  107M 0s
 49100K .......... .......... .......... .......... .......... 91% 21,0M 0s
 49150K .......... .......... .......... .......... .......... 91%  103M 0s
 49200K .......... .......... .......... .......... .......... 91% 25,1M 0s
 49250K .......... .......... .......... .......... .......... 91%  130M 0s
 49300K .......... .......... .......... .......... .......... 91% 16,2M 0s
 49350K .......... .......... .......... .......... .......... 91% 99,2M 0s
 49400K .......... .......... .......... .......... .......... 91% 13,1M 0s
 49450K .......... .......... .......... .......... .......... 91% 38,3M 0s
 49500K .......... .......... .......... .......... .......... 91% 66,3M 0s
 49550K .......... .......... .......... .......... .......... 92% 16,6M 0s
 49600K .......... .......... .......... .......... .......... 92%  121M 0s
 49650K .......... .......... .......... .......... .......... 92% 25,9M 0s
 49700K .......... .......... .......... .......... .......... 92% 53,1M 0s
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 49800K .......... .......... .......... .......... .......... 92% 65,1M 0s
 49850K .......... .......... .......... .......... .......... 92% 17,1M 0s
 49900K .......... .......... .......... .......... .......... 92%  229M 0s
 49950K .......... .......... .......... .......... .......... 92% 20,1M 0s
 50000K .......... .......... .......... .......... .......... 92% 79,5M 0s
 50050K .......... .......... .......... .......... .......... 92% 14,0M 0s
 50100K .......... .......... .......... .......... .......... 93% 16,5M 0s
 50150K .......... .......... .......... .......... .......... 93% 62,6M 0s
 50200K .......... .......... .......... .......... .......... 93% 29,8M 0s
 50250K .......... .......... .......... .......... .......... 93% 53,4M 0s
 50300K .......... .......... .......... .......... .......... 93% 19,2M 0s
 50350K .......... .......... .......... .......... .......... 93% 80,8M 0s
 50400K .......... .......... .......... .......... .......... 93% 18,2M 0s
 50450K .......... .......... .......... .......... .......... 93% 37,3M 0s
 50500K .......... .......... .......... .......... .......... 93% 25,5M 0s
 50550K .......... .......... .......... .......... .......... 93%  159M 0s
 50600K .......... .......... .......... .......... .......... 93% 18,6M 0s
 50650K .......... .......... .......... .......... .......... 94%  156M 0s
 50700K .......... .......... .......... .......... .......... 94% 20,1M 0s
 50750K .......... .......... .......... .......... .......... 94% 19,6M 0s
 50800K .......... .......... .......... .......... .......... 94%  180M 0s
 50850K .......... .......... .......... .......... .......... 94%  193M 0s
 50900K .......... .......... .......... .......... .......... 94% 16,5M 0s
 50950K .......... .......... .......... .......... .......... 94% 13,9M 0s
 51000K .......... .......... .......... .......... .......... 94% 87,7M 0s
 51050K .......... .......... .......... .......... .......... 94% 34,2M 0s
 51100K .......... .......... .......... .......... .......... 94% 55,4M 0s
 51150K .......... .......... .......... .......... .......... 94% 17,1M 0s
 51200K .......... .......... .......... .......... .......... 95% 45,1M 0s
 51250K .......... .......... .......... .......... .......... 95% 17,1M 0s
 51300K .......... .......... .......... .......... .......... 95%  119M 0s
 51350K .......... .......... .......... .......... .......... 95% 26,2M 0s
 51400K .......... .......... .......... .......... .......... 95% 80,2M 0s
 51450K .......... .......... .......... .......... .......... 95% 18,6M 0s
 51500K .......... .......... .......... .......... .......... 95% 69,3M 0s
 51550K .......... .......... .......... .......... .......... 95% 19,2M 0s
 51600K .......... .......... .......... .......... .......... 95% 53,2M 0s
 51650K .......... .......... .......... .......... .......... 95% 25,9M 0s
 51700K .......... .......... .......... .......... .......... 95% 24,2M 0s
 51750K .......... .......... .......... .......... .......... 96% 53,8M 0s
 51800K .......... .......... .......... .......... .......... 96% 49,7M 0s
 51850K .......... .......... .......... .......... .......... 96% 23,2M 0s
 51900K .......... .......... .......... .......... .......... 96% 61,0M 0s
 51950K .......... .......... .......... .......... .......... 96% 15,9M 0s
 52000K .......... .......... .......... .......... .......... 96% 19,0M 0s
 52050K .......... .......... .......... .......... .......... 96% 69,9M 0s
 52100K .......... .......... .......... .......... .......... 96%  146M 0s
 52150K .......... .......... .......... .......... .......... 96% 16,1M 0s
 52200K .......... .......... .......... .......... .......... 96% 92,5M 0s
 52250K .......... .......... .......... .......... .......... 97% 22,3M 0s
 52300K .......... .......... .......... .......... .......... 97% 46,5M 0s
 52350K .......... .......... .......... .......... .......... 97% 18,3M 0s
 52400K .......... .......... .......... .......... .......... 97% 25,9M 0s
 52450K .......... .......... .......... .......... .......... 97% 69,9M 0s
 52500K .......... .......... .......... .......... .......... 97% 13,4M 0s
 52550K .......... .......... .......... .......... .......... 97% 86,4M 0s
 52600K .......... .......... .......... .......... .......... 97% 27,6M 0s
 52650K .......... .......... .......... .......... .......... 97% 84,5M 0s
 52700K .......... .......... .......... .......... .......... 97% 19,4M 0s
 52750K .......... .......... .......... .......... .......... 97%  104M 0s
 52800K .......... .......... .......... .......... .......... 98% 22,4M 0s
 52850K .......... .......... .......... .......... .......... 98% 74,7M 0s
 52900K .......... .......... .......... .......... .......... 98% 20,3M 0s
 52950K .......... .......... .......... .......... .......... 98% 66,6M 0s
 53000K .......... .......... .......... .......... .......... 98% 17,5M 0s
 53050K .......... .......... .......... .......... .......... 98%  149M 0s
 53100K .......... .......... .......... .......... .......... 98% 14,0M 0s
 53150K .......... .......... .......... .......... .......... 98% 97,7M 0s
 53200K .......... .......... .......... .......... .......... 98% 15,1M 0s
 53250K .......... .......... .......... .......... .......... 98%  226M 0s
 53300K .......... .......... .......... .......... .......... 98% 24,2M 0s
 53350K .......... .......... .......... .......... .......... 99% 12,8M 0s
 53400K .......... .......... .......... .......... .......... 99%  231M 0s
 53450K .......... .......... .......... .......... .......... 99% 23,0M 0s
 53500K .......... .......... .......... .......... .......... 99%  213M 0s
 53550K .......... .......... .......... .......... .......... 99% 16,5M 0s
 53600K .......

Unzipping Dataset
Archive:  cats-dogs-data.zip
Removing .zip file


... .......... .......... .......... .......... 99% 47,1M 0s
 53650K .......... .......... .......... .......... .......... 99% 20,0M 0s
 53700K .......... .......... .......... .......... .......... 99% 73,2M 0s
 53750K .......... .......... .......... .......... .......... 99% 26,8M 0s
 53800K .......... .......... .......... .......... .......... 99% 56,3M 0s
 53850K .......... .......... .......... .......... .......... 99% 6,71M 0s
 53900K .........                                             100%  132M=1,9s

2021-10-06 11:26:22 (27,8 MB/s) - ‘cats-dogs-data.zip’ saved [55203029/55203029]

replace cats-dogs-data/.DS_Store? [y]es, [n]o, [A]ll, [N]one, [r]ename:  NULL
(EOF or read error, treating as "[N]one" ...)

Here's the keras implementation for a great performance result:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Flatten, Dense, GlobalAveragePooling2D
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam

TARGET_SHAPE = (224, 224, 3)
TRAIN_PATH = 'cats-dogs-data/train'
VALID_PATH = 'cats-dogs-data/valid'

datagen = ImageDataGenerator(preprocessing_function=preprocess_input)
train_gen = datagen.flow_from_directory(TRAIN_PATH, 
                                        target_size=TARGET_SHAPE[:2], 
                                        class_mode='sparse')
valid_gen = datagen.flow_from_directory(VALID_PATH, 
                                        target_size=TARGET_SHAPE[:2], 
                                        class_mode='sparse',
                                        shuffle=False)

base_model = ResNet50(include_top=False, input_shape=TARGET_SHAPE)

for layer in base_model.layers:
    layer.trainable=False
    
model = Sequential([base_model,
                    GlobalAveragePooling2D(),
                    Dense(1024, activation='relu'),
                    Dense(2, activation='softmax')])

Found 2000 images belonging to 2 classes.
Found 400 images belonging to 2 classes.

model.compile(optimizer=Adam(learning_rate=1e-4), loss='sparse_categorical_crossentropy', metrics=['accuracy'])

model.fit(train_gen, epochs=3, validation_data=valid_gen)

Epoch 1/3


2021-10-06 11:29:26.088412: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-10-06 11:29:26.256 python[95381:1139332] -[MPSGraph adamUpdateWithLearningRateTensor:beta1Tensor:beta2Tensor:epsilonTensor:beta1PowerTensor:beta2PowerTensor:valuesTensor:momentumTensor:velocityTensor:maximumVelocityTensor:gradientTensor:name:]: unrecognized selector sent to instance 0x2a93793b0

By looking at val_accuracy we can confirm the results seems great. Let's also plot some other metrics:

from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix
import seaborn as sns
y_pred = model.predict(valid_gen)
y_test = valid_gen.classes
roc = roc_auc_score(y_test, y_pred[:, 1])
print("ROC AUC Score", roc)

ROC AUC Score 0.9989

cm=confusion_matrix(y_test, y_pred.argmax(axis=1))
sns.heatmap(cm, annot=True, fmt='g')

<matplotlib.axes._subplots.AxesSubplot at 0x7fcc41c57090>

png

Although we got an almost perfect clssifier, there are multiple details that someone who is coming from sklearn has to be careful when using Keras, for example:

Correctly setup the Data Generator
Fine tune the learning rate
Adjust the batch size

Now let's replicate the same results using deepfeatx:

from deepfeatx.image import ImageFeatureExtractor
from sklearn.linear_model import LogisticRegression

TRAIN_PATH = 'cats-dogs-data/train'
VALID_PATH = 'cats-dogs-data/valid'

fe = ImageFeatureExtractor()

train=fe.extract_features_from_directory(TRAIN_PATH, 
                                         classes_as_folders=True,
                                         export_class_names=True)
test=fe.extract_features_from_directory(VALID_PATH, 
                                         classes_as_folders=True,
                                         export_class_names=True)

X_train, y_train = train.drop(['filepaths', 'classes'], axis=1), train['classes']
X_test, y_test = test.drop(['filepaths', 'classes'], axis=1), test['classes']
lr = LogisticRegression().fit(X_train, y_train)

Found 2000 images belonging to 2 classes.


2021-10-06 11:27:40.528937: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.


63/63 [==============================] - 22s 350ms/step
Found 400 images belonging to 2 classes.
13/13 [==============================] - 4s 351ms/step


/Users/wittmann/miniforge3/envs/mlp/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

roc_auc_score(y_test, lr.predict_proba(X_test)[:, 1])

0.9996

import seaborn as sns
cm=confusion_matrix(y_test, lr.predict(X_test))
sns.heatmap(cm, annot=True, fmt='g')

<AxesSubplot:>

png

Even though the code is smaller, is still as powerful as the keras code and also very flexible. The most important part is the feature extraction, which deepfeatx take care for us, and the rest can be performed as any other ML problem.

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Development Status
- 3 - Alpha
Intended Audience
- Developers
License
- OSI Approved :: Apache Software License
Natural Language
- English
Programming Language

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Oct 9, 2021

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0.1.2

Oct 6, 2021

0.1.0b0 pre-release

Apr 10, 2021

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deepfeatx 0.1.2

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`deepfeatx`: Deep Learning Feature Extractor of Images using Transfer Learning Models

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Why this project has been created

Usage

Extracting features from an image

Extracting Features from a Folder with Images

Extracting Features from a directory having one sub-folder per class

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Cats vs Dogs using Keras vs `deepfeatx`

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deepfeatx: Deep Learning Feature Extractor of Images using Transfer Learning Models

Install

Why this project has been created

Usage

Extracting features from an image

Extracting Features from a Folder with Images

Extracting Features from a directory having one sub-folder per class

Examples

Cats vs Dogs using Keras vs deepfeatx

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`deepfeatx`: Deep Learning Feature Extractor of Images using Transfer Learning Models

Cats vs Dogs using Keras vs `deepfeatx`